"""simple docstring"""


a				=256
# Modulus to hash a string
a				=100_0003





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Any:




						'''simple docstring'''



						lowerCamelCase__   =len(__lowerCAmelCase							)
						lowerCamelCase__   =len(__lowerCAmelCase							)
						if p_len > t_len:
												return False

						lowerCamelCase__   =0
						lowerCamelCase__   =0
						lowerCamelCase__   =1

						# Calculating the hash of pattern and substring of text
						for i in range(__lowerCAmelCase							):
												lowerCamelCase__   =(ord(pattern[i]							) + p_hash * alphabet_size) % modulus
												lowerCamelCase__   =(ord(text[i]							) + text_hash * alphabet_size) % modulus
												if i == p_len - 1:
																		continue
												lowerCamelCase__   =(modulus_power * alphabet_size) % modulus

						for i in range(0  ,       t_len - p_len + 1							):
												if text_hash == p_hash and text[i : i + p_len] == pattern:
																		return True
												if i == t_len - p_len:
																		continue
												# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
												lowerCamelCase__   =(
												    (text_hash - ord(text[i]							) * modulus_power) * alphabet_size
												    + ord(text[i + p_len]							)
												) % modulus
						return False





def      lowerCamelCase_					(   )				->   Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__   ="abc1abc12"
						lowerCamelCase__   ="alskfjaldsabc1abc1abc12k23adsfabcabc"
						lowerCamelCase__   ="alskfjaldsk23adsfabcabc"
						assert rabin_karp(__lowerCAmelCase  ,       __lowerCAmelCase							) and not rabin_karp(__lowerCAmelCase  ,       __lowerCAmelCase							)

						# Test 2)
						lowerCamelCase__   ="ABABX"
						lowerCamelCase__   ="ABABZABABYABABX"
						assert rabin_karp(__lowerCAmelCase  ,       __lowerCAmelCase							)

						# Test 3)
						lowerCamelCase__   ="AAAB"
						lowerCamelCase__   ="ABAAAAAB"
						assert rabin_karp(__lowerCAmelCase  ,       __lowerCAmelCase							)

						# Test 4)
						lowerCamelCase__   ="abcdabcy"
						lowerCamelCase__   ="abcxabcdabxabcdabcdabcy"
						assert rabin_karp(__lowerCAmelCase  ,       __lowerCAmelCase							)

						# Test 5)
						lowerCamelCase__   ="Lü"
						lowerCamelCase__   ="Lüsai"
						assert rabin_karp(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   ="Lue"
						assert not rabin_karp(__lowerCAmelCase  ,       __lowerCAmelCase							)
						print("Success."							)


if __name__ == "__main__":
	test_rabin_karp()



import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Union[str, Any]         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				if "model" in sd.keys():
								__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)['''model''']

				# pop unnecessary weights
				__lowercase					     =				[
				    '''decoder.version''',
				    '''decoder.output_projection.weight''',
				]
				for key in keys_to_delete:
								if key in sd:
												sd.pop(lowercase				)

				__lowercase					     =				{
				    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
				    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
				    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
				    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
				}
				for old_key, new_key in keys_to_rename.items():
								if old_key in sd:
												__lowercase					     =				sd.pop(lowercase				)

				__lowercase					     =				list(sd.keys()				)
				for key in keys:
								if ".qkv_proj." in key:
												__lowercase					     =				sd[key]
												# We split QKV in separate Q,K,V

												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.q_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.k_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.v_proj.'''				)

												__lowercase					     =				value.shape[0]
												assert depth % 3 == 0
												# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
												# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
												__lowercase     ,	__lowercase     ,	__lowercase					     =				torch.split(lowercase				,     depth // 3				,     dim=0				)

												__lowercase					     =				q
												__lowercase					     =				k
												__lowercase					     =				v
												del sd[key]

				return sd





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=None				):







				"""simple docstring"""



				__lowercase					     =				load_checkpoint(lowercase				)

				if config is not None:
								__lowercase					     =				OPTConfig.from_pretrained(lowercase				)
				else:
								__lowercase					     =				OPTConfig()

				__lowercase					     =				OPTModel(lowercase				).half().eval()
				model.load_state_dict(lowercase				)

				# Check results
				Path(lowercase				).mkdir(exist_ok=lowercase				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--fairseq_path""",
		    type=str,
		    help=(
		        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
		        """ https://huggingface.co/models?other=opt_metasq"""
		    ),
		)
		parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
		__a    :	Any         =    parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)